Koba Khitalishvili KobaKhit

## reddit-posts.html
<!-- Produces a responsive list of top ten posts from a subreddit /worldnews. Working jsfiddle http://jsfiddle.net/KobaKhit/t42zkbnk/ -->
<div id="posts">
    <h2> Today's top ten news <small>from <a href = '//reddit.com/r/worldnews' target = '_blank'>/r/worldnews</a></small></h2>
    <hr>
    <ul class="list-unstyled"></ul>
</div>

<!-- JS -->
<script src="https://rawgit.com/sahilm/reddit.js/master/reddit.js"></script>
<script src="https://code.jquery.com/jquery-2.1.3.min.js"></script>

## Large dataframe to csv in chunks in R
df = read.csv("your-df.csv")

# Number of items in each chunk
elements_per_chunk = 100000

# List of vectors [1] 1:100000, [2] 100001:200000, ...
l = split(1:nrow(df), ceiling(seq_along(1:nrow(df))/elements_per_chunk))

# Write large data frame to csv in chunks
fname = "inventory-cleaned.csv"

## unnest_byseat.R
library(tidyr)

setwd("~/Desktop/unnest")

fname = "file-name.csv"
df = read.csv(paste0(fname,'.csv'), stringsAsFactors = F)

df$seats =
sapply(1:nrow(df), function(x) {
 seats =  c(df[x,]$first_seat,df[x,]$last_seat)

## reddit_posts_and_comments.py
class Reddit():
    def __init__(self,client_id, client_secret,user_agent='My agent'):
        self.reddit = praw.Reddit(client_id=client_id,
                                  client_secret=client_secret,
                                  user_agent=user_agent)

    def get_comments(self, submission):
        # get comments information using the Post as a starting comment
        comments = [RedditComment(author=submission.author,
                                 commentid = submission.postid,

## visualforce_embed_with_user.html
<apex:page >
<html>
  <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.7.2/jquery.min.js"></script>

  <!-- User Id in a span -->
  <span id = 'user' style = 'display: none;'>
    <apex:outputText label="Account Owner" value="{!$User.Id}"></apex:outputText>
  </span>

  <!-- Embed placeholder -->

## repartition_pyspark_dataframe.py
from pyspark.sql.functions import monotonically_increasing_id, row_number
from pyspark.sql import Window
from functools import reduce

def partitionIt(size, num):
    '''
    Create a list of partition indices each of size num where number of groups is ceiling(len(seq)/num)

    Args:
        size (int): number of rows/elemets

## hmtl_table_parser.py
# http://srome.github.io/Parsing-HTML-Tables-in-Python-with-BeautifulSoup-and-pandas/
class HTMLTableParser:
    @staticmethod
    def get_element(node):
        # for XPATH we have to count only for nodes with same type!
        length = len(list(node.previous_siblings)) + 1
        if (length) > 1:
            return '%s:nth-child(%s)' % (node.name, length)
        else:
            return node.name

## sql-spines.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                KobaKhit
                / sql-spines.md
            
            
              Created
              February 8, 2024 02:58
            
              
                Examples of generating spines/dates in SQL. Assisted by Caleb Kassa.
              
          
    Spines in SQL

Given a starting date 2024-02-01 I would like to generate 7 days into the future until February 8th (2024-02-08), ex.g.


dt


2024-02-01


2024-02-02


2024-02-03


2024-02-04


## snowflake-classify-text.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                KobaKhit
                / snowflake-classify-text.md
            
            
              Last active
              September 16, 2024 16:06
            
              
                Text Classification in Snowflake SQL
              
          
    Classifying Text in Snowflake SQL

With LLMs becoming available in Snowflake as part of their Cortex suite of products in this piece we will explore what the experience is like when classifying text. First of all, Snowflake has native CLASSIFY_TEXT function that does exactly what it says when given a piece of text and an array of possible categories. Second, one could classify text using emebeddings (EMBED_TEXT_768) and similarity to possible categories calculated by one of the distance function like cosine similarity (VECTOR_COSINE_SIMILARITY). Finally, when going the embeddings + similarity route we could use a cross join with a categories table or create a column for each category's similarity score and then assign the greatest one. So we have thre

  
## app.py
import plotly.express as px
import streamlit as st

# Sample data
df = px.data.iris()

# Create the Plotly figure
fig = px.scatter(df,
                 x="sepal_width",
                y="sepal_length",
	<!-- Produces a responsive list of top ten posts from a subreddit /worldnews. Working jsfiddle http://jsfiddle.net/KobaKhit/t42zkbnk/ -->
	<div id="posts">
	<h2> Today's top ten news <small>from <a href = '//reddit.com/r/worldnews' target = '_blank'>/r/worldnews</a></small></h2>
	<hr>
	<ul class="list-unstyled"></ul>
	</div>

	<!-- JS -->
	<script src="https://rawgit.com/sahilm/reddit.js/master/reddit.js"></script>
	<script src="https://code.jquery.com/jquery-2.1.3.min.js"></script>
	df = read.csv("your-df.csv")

	# Number of items in each chunk
	elements_per_chunk = 100000

	# List of vectors [1] 1:100000, [2] 100001:200000, ...
	l = split(1:nrow(df), ceiling(seq_along(1:nrow(df))/elements_per_chunk))

	# Write large data frame to csv in chunks
	fname = "inventory-cleaned.csv"
	library(tidyr)

	setwd("~/Desktop/unnest")

	fname = "file-name.csv"
	df = read.csv(paste0(fname,'.csv'), stringsAsFactors = F)

	df$seats =
	sapply(1:nrow(df), function(x) {
	seats = c(df[x,]$first_seat,df[x,]$last_seat)
	class Reddit():
	def __init__(self,client_id, client_secret,user_agent='My agent'):
	self.reddit = praw.Reddit(client_id=client_id,
	client_secret=client_secret,
	user_agent=user_agent)

	def get_comments(self, submission):
	# get comments information using the Post as a starting comment
	comments = [RedditComment(author=submission.author,
	commentid = submission.postid,
	<apex:page >
	<html>
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.7.2/jquery.min.js"></script>

	<!-- User Id in a span -->
	<span id = 'user' style = 'display: none;'>
	<apex:outputText label="Account Owner" value="{!$User.Id}"></apex:outputText>
	</span>

	<!-- Embed placeholder -->
	from pyspark.sql.functions import monotonically_increasing_id, row_number
	from pyspark.sql import Window
	from functools import reduce

	def partitionIt(size, num):
	'''
	Create a list of partition indices each of size num where number of groups is ceiling(len(seq)/num)

	Args:
	size (int): number of rows/elemets
	# http://srome.github.io/Parsing-HTML-Tables-in-Python-with-BeautifulSoup-and-pandas/
	class HTMLTableParser:
	@staticmethod
	def get_element(node):
	# for XPATH we have to count only for nodes with same type!
	length = len(list(node.previous_siblings)) + 1
	if (length) > 1:
	return '%s:nth-child(%s)' % (node.name, length)
	else:
	return node.name
	import plotly.express as px
	import streamlit as st

	# Sample data
	df = px.data.iris()

	# Create the Plotly figure
	fig = px.scatter(df,
	x="sepal_width",
	y="sepal_length",